基于BPSO-KNN算法的被动声呐目标分类识别技术研究

Passive sonar target classification and recognition technique based on BPSO-KNN algorithm

摘要: 以提取得到的被动声呐目标功率谱特征为基础，采用二进制粒子群（Binary Particle Swarm Optimization，BPSO）优化算法和k最近邻（k-Nearest Neighbor，KNN）分类算法相结合的BPSO-KNN算法进行特征选择和参数优化，分别用KNN分类算法和BPSO-KNN分类算法对实际得到的四类海上被动声呐目标进行分类识别。结果表明，BPSO-KNN算法可对提取的功率谱特征进行特征优化选择，并对KNN分类器进行参数优化，提高了对四类目标的分类精度。该算法在被动声呐目标分类识别方面有参考价值。

Abstract: Based on the obtained power spectrum characteristics of passive sonar target, the BPSO-KNN algorithm combining binary particle swarm optimization (BPSO) algorithm and k-nearest neighbor (KNN) classification algorithm is used to carry out feature selection and parameter optimization. The comparative study is made for four types of passive sonar target recognition by using the KNN classification algorithm and the BPSO-KNN algorithm. Experimental results show that the BPSO-KNN is an effective method for both power spectrum characteristics reduction and KNN algorithm parameter optimization. And the classification accuracy of the four types of targets is improved, which shows that the algorithm has reference value in passive sonar target classification and recognition.